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Record W2548551707 · doi:10.1080/21645698.2016.1228516

A critical assessment of regulatory triggers for products of biotechnology: Product vs. process

2016· review· en· W2548551707 on OpenAlexaboutno aff
Alan McHughen

Bibliographic record

VenueGM crops & food · 2016
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural biotechnologyStatuteProcess (computing)European unionBiotechnologyProduct (mathematics)BusinessPoliticsAgricultureRisk analysis (engineering)Political scienceInternational tradeBiologyLawComputer science

Abstract

fetched live from OpenAlex

Regulatory policies governing the safety of genetic engineering (rDNA) and the resulting products (GMOs) have been contentious and divisive, especially in agricultural applications of the technologies. These tensions led to vastly different approaches to safety regulation in different jurisdictions, even though the intent of regulations-to assure public and environmental safety-are common worldwide, and even though the international scientific communities agree on the basic principles of risk assessment and risk management. So great are the political divisions that jurisdictions cannot even agree on the appropriate triggers for regulatory capture, whether product or process. This paper reviews the historical policy and scientific implications of agricultural biotechnology regulatory approaches taken by the European Union, USA and Canada, using their respective statutes and regulations, and then critically assesses the scientific underpinnings of each.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.005
Scholarly communication0.0040.008
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.369
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations72
Published2016
Admission routes1
Has abstractyes

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